Vocabulary compression involves reducing the number of unique words or symbols in a dataset while preserving the essential meaning or information, often used in natural language processing to improve computational efficiency and model performance. This technique can enhance generalization by focusing on core concepts and minimizing noise from rare or irrelevant terms.